Data Engineer & Insights Analyst - AWS / Longitudinal Data Systems

Data Engineer & Insights Analyst - AWS / Longitudinal Data Systems

Location

Dauphin County, PA; finalist interviews in Harrisburg, PA

Work Arrangement

100% Remote (government equipment must remain in the United States)

Schedule

8 hours/day, 5 days/week (40 hours/week)

Interview

Two rounds

Microsoft Teams first; in-person interview for finalists in Harrisburg, PA

Contract

Nov 23, 2026 - Dec 31, 2026 (39 calendar days). Historically, these roles are extended for an additional year.

Visas Accepted

USC, GC, GC EAD, H1B

Role Overview

Our client is seeking a Data Engineer & Insights Analyst to support a longitudinal data system that turns complex information into reliable, actionable insights for program delivery, policy research, operational improvement, and executive decision-making. This role combines cloud data engineering with analytical judgment to support complex and confidential datasets.

The position will design and optimize data pipelines, integrate and transform data across systems, implement quality and governance controls, and support data conversion during system modernization. Work is remote, with an in-person second-round interview for finalists in Harrisburg, Pennsylvania.

Primary Responsibilities

• Create and maintain scalable, reliable data pipeline architecture.
• Design and implement cloud-based data solutions using AWS services such as S3, Glue, Redshift, EMR, Athena, Kinesis, and Lambda.
• Build and maintain orchestration workflows using Apache Airflow, AWS Step Functions, or similar services.
• Assemble large, complex datasets that satisfy functional and non-functional business requirements.
• Engineer analytical data environments that support statistical analysis, predictive modeling, machine learning, and evidence-building.
• Partner with data scientists, research analysts, and policy analysts to support reproducible research and statistical modeling.
• Automate manual processes, optimize data delivery, and redesign infrastructure for scalability.
• Build infrastructure for extracting, transforming, and loading data from varied sources using SQL and big-data technologies.
• Develop analytics tools and data products that support educational and workforce outcomes, operational efficiency, and business performance measures.
• Develop and maintain data pipeline monitoring, alerting, logging, and data-quality validation frameworks.
• Build dashboards, reports, analytical models, and decision-support products for executive and nontechnical users.
• Work with Executive, Data, Design, and Support teams to resolve data-related technical issues and support infrastructure needs.
• Apply data governance, privacy, security, and interagency data-sharing requirements.
• Support modernization-related data conversions and collaborate with data professionals to improve systems and analytics capabilities.

Required Technical Skills

• Hands-on proficiency with cloud-based data services such as AWS S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, and DynamoDB.
• Proficiency with object-oriented or object-function scripting languages such as Python, R, Java, C++, and Scala.
• Advanced SQL and relational/NoSQL database capabilities, including Oracle, MS SQL Server, Postgres, and Cassandra.
• Experience with Git version control and CI/CD pipelines.
• Knowledge of dimensional modeling, data vault architecture, and star/snowflake schemas.
• Familiarity with data cataloging and metadata tools such as AWS Glue Data Catalog.
• Understanding of data governance, lineage, and data quality frameworks.
• Knowledge of REST API design and integration patterns.
• Experience with big-data tools such as Hadoop, Spark, and Kafka.
• Experience with pipeline orchestration and workflow-management tools such as Azkaban, Luigi, and Airflow.
• Experience with data integration solutions such as Informatica, MuleSoft, Talend, and TIBCO.
• Experience with stream-processing tools such as Storm and Spark Streaming.
• Experience building and optimizing large data pipelines, architectures, and datasets, and delivering dashboards and decision-support analytics.
• Experience with student-level higher education data and connecting education records with employment or wage data for outcomes research.
• Root-cause analysis across internal/external datasets and processes, including unstructured data and data transformation, metadata, dependency, and workload management.
• Working knowledge of messaging, stream processing, scalable big-data stores, project organization, and cross-functional delivery.